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The analysis of multivariate longitudinal data: A review

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Verbeke, Geert, Fieuws, Steffen, Molenberghs, Geert, Davidian, Marie·2014·Statistical Methods in Medical Research

DOI 10.1177/0962280212445834PMC PMC3404254

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Abstract

Longitudinal experiments often involve multiple outcomes measured repeatedly within a set of study participants. While many questions can be answered by modeling the various outcomes separately, some questions can only be answered in a joint analysis of all of them. In this article, we will present a review of the many approaches proposed in the statistical literature. Four main model families will be presented, discussed and compared. Focus will be on presenting advantages and disadvantages of the different models rather than on the mathematical or computational details.

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Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
An Introduction to Copulas
Nelsen, R. B. (1999) · Lecture notes in statistics · doi:10.1007/978-1-4757-3076-0 · Cited by 7,777
Analysis of Longitudinal Data
Everitt, B. S. (2001) · Psychology Press eBooks · doi:10.4324/9781410605542-11 · Cited by 6,877
Multilevel Statistical Models
Goldstein, H. (2006) · Technometrics · doi:10.1198/tech.2006.s417 · Cited by 4,960
Approximate Inference in Generalized Linear Mixed Models
Breslow, N. E., & Clayton, D. G. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594284 · Cited by 4,662
Fonctions de répartition à N dimensions et leurs marges
Sklar, M. (1959) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 4,271
Linear Mixed Models for Longitudinal Data
Verbeke, G. (1997) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2294-1_3 · Cited by 3,506
Self-citation
Analysis of Longitudinal Data
Diggle, P. J., Heagerty, P. J., Liang, K., & Zeger, S. L. (2002) · doi:10.1093/oso/9780198524847.001.0001 · Cited by 3,388
Multilevel Statistical Models
Courgeau, D., & Goldstein, H. (1997) · Population · doi:10.2307/1534624 · Cited by 2,596

Flagged first, then by citations

Show all 102 references

1 of the 102 references couldn’t be matched to a known paper.

Research works printed but not linked: 9

Shock N, 1984, National Institutes of Health publication 84-2450
O'Brien L, 2004, Appl Stat, 53, 177
Sklar A, 1959, Publications de l'Institut de Statistique de l'Université de Paris, 8, 229
Bryk A, 1992, Hierarchical linear models in social and behavioral research: applications and data analysis methods
Fitzmaurice G, 2009, Handbooks of Modern Statistical Methods
Arnold B, 1991, Sankhya: Ind J Stat Series B, 53, 233
Fieuws S, 2006, Appl Stat, 55, 1
Liu L, 2005, Biometrics, 3
Blozis S, 2006, Longitudinal models in the behavioural and related sciences, 189

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Self-citation

Authors: Verbeke, G., Fieuws, S., Molenberghs, G., & Davidian, M.
Affiliations: Hasselt University (BE)
KU Leuven (BE)
North Carolina State University (US)
Corresponding author: Verbeke, Geert
ORCID: Verbeke, Geert
Fieuws, Steffen
Molenberghs, Geert
Davidian, Marie
Venue: Statistical Methods in Medical Research
Publisher: SAGE Publications
Published: 2012-04-20
Volume: 23
Issue: 1
Pages: 42-59
ISSN: 0962-2802 (ISSN-L)
1477-0334
DOI: 10.1177/0962280212445834
PMC: PMC3404254
OpenAlex: W2004277545
Cited by: 290
References: 102
Citations by year: 2026: 8 · 2025: 25 · 2024: 17 · 2023: 19 · 2022: 30 · 2021: 43 · 2020: 34 · 2019: 26 · 2018: 19 · 2017: 19 · 2016: 20 · 2015: 14 · 2014: 13 · 2013: 3
FWCI: 9.16
Type: journal article
Language: en
Open access: green
europepmc.org
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Statistical Methods in Medical Research (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Document Server@UHasselt (UHasselt) (submittedVersion, other-oa)
Lirias (submittedVersion)
Funders: National Cancer Institute (R01CA085848, P01CA142538)
National Institute of Allergy and Infectious Diseases (R37AI031789)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: multivariate longitudinal data, joint modeling, multiple outcomes, repeated measures, longitudinal experiment, model comparison
MeSH terms: Hearing, Humans, Longitudinal Studies, Models, Statistical, Multivariate Analysis

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The analysis of multivariate longitudinal data: A review

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